Advances in Industrial Engineering
Year:2018 | Volume:52 | Issue:1 |Papers Count:12

There is considerable interest in the use of Statistical Process Control (SPC) in healthcare in the recent years because SPC tools lead to continual improvement in healthcare process. Control chart is one of the main tools of the SPC، however، they usually signal the out-of-control status with delay respect to real time of the change known as change point. Hence، change point estimation is important in healthcare with considering relationship between quality engineering and hospital epidemiology. There are varieties of quality characteristics in healthcare which are should be monitored over time. Therefore، in this paper، first، g and h control charts are described since these control charts are famous tools for monitoring events in healthcare. Then، corresponding step change point estimators using Maximum Likelihood Estimation (MLE) are proposed. In this regard، Mont Carlo simulation is used to evaluate performances of the proposed estimators based on accuracy and precision measures under all kinds of shifts. In addition، cardinality and coverage probability of confidence set are presented for the proposed estimators based on the logarithm of the likelihood function. The simulation studies are conducted under different magnitudes of the step shifts to evaluate performances of the proposed change point estimators in both control charts. The results show that as the magnitude of the step change in the parameter of the distributions increase، the performance of the proposed change point estimators improve significantly in terms of precision and accuracy measures. Also، cardinality and coverage percentage of confidence set estimators are calculated and plotted to show the relationship among these measures and increasing and decreasing shifts under the given reference values. In addition، the simulation studies demonstrate that as the magnitude of the step change in the parameters of the distributions enlarge، cardinality of set estimators reduces and coverage percentage of confidence set estimators increases under the given references values. In general، results show that the proposed change point estimators perform satisfactory under all types of shifts. Finally، the performance of one of the proposed change point estimators is illustrated through an applied example in healthcare.

Nowadays، designing and implementing the systems with premier features and higher reliability is deemed to be a basic principle for the engineers and users، because regarding this point can result in the proper use of a system during its lifetime. In today’ s competitive world، offering a system with lower total expense، given that its high reliability is maintained، can make the company popular with the customers. In the current research، regarding the discounts based on the total number of the orders، a compound of components with active redundancy strategy and ready to work is determined، in such a way that the cost of purchasing the components is minimized، besides optimizing the whole system’ s reliability. In the research، the probable approach in invention can be defined that two models examined with cost minimization and Reliability maximization aims that they are، respectively، the probable restrictions on the cost and Reliability. With regard to the model’ s proper treatment of changes in effective factors proposed in the model، it is concluded that this model is exploitable for optimizing stability in mass production industries where applying the global discount policy leads to some benefits.

The shortage of data is one of the most important problems in system modeling and optimization in industrial applications. Typical modeling techniques are unable to properly model a system with a limited dataset. In this paper, a modeling method for optimization of these systems is proposed. The proposed method has two main steps. In the first step, the model is employed to generate data using neural network. This model determines the correspondence input of each output. In the second step, optimization of the generated model is performed using genetic algorithm. Inputs leading to the specified output can be estimated using the proposed system. Optimality of the system can be explained by an evaluation function. The proposed method was evaluated in two different experiments on a time series and a real data. Results of the experiments were analyzed using mean square error. The experimental results show the capability of the proposed method in system modeling and optimization.

This paper studies inverse 2-center location problem by increasing and decreasing the edge length on unweighted tree networks. The goal is to increase and decrease the edge lengths at minimum total cost subject to given modification bounds such that predetermined vertices becomes absolute 2-center. In order to demonstrate the practical application of this issue، we consider Bojnord urban network and two important fire stations of the city as center locations. Moreover، an example is generated for computational analysis. The results show when predetermined vertices are close to ends of tree، a higher cost is imposed as well as in most cases it is impossible.

In this study، Two-stage assembly flow shop problem (TAFSP) considering aging effects of the machines and preventive maintenance activities is investigated. At the first stage، m-1 parallel machines process parts of each jobs and at the second stage related parts of the jobs are assembled by one assembly machine. As the machines work on the jobs their tools get aged. Aging effects on the machines causes that they will not be able to complete the jobs in the same time could as they were new or when they are operating jobs immediately after their preventive maintenance activity. Processing times of the job are related to the positions in which it is located after the last preventive maintenance. The job that is operated in a position immediately after the preventive maintenance activity on a machine has its standard processing time، however the processing time of the jobs operated in the further positions increase based on the number of the positions. The machines return to the initial condition after each preventive maintenance activity. The objective is to schedule the jobs on the machines and determine when the preventive maintenance activities get done on them in order to minimize the total weighted tardiness and maintenance costs. An integer mathematical model is presented for the problem and its validation is shown by solving an example in small scale. Since two-stage assembly flow shop problem is Np-hard، in order to solve the problem in medium and large scale two meta-heuristic algorithms، Hybrid Genetic Algorithm (HGA) and Hybrid Particle swarm Optimization (HPSO)، are proposed. These algorithms are hybrids version of genetic algorithm and particle swarm optimization representatively with simulated annealing. The algorithms are tuned by Taguchi method and are used to solve many numerical examples. Finally statistical analysis illustrates that performance of HPSO is better than HGA.

Maintenance routing is an important and complicated issue in aviation systems. Determining factors affecting delay and cost of flights is important as they cause customer dissatisfaction. Ramping and maintenance directly affect flights delay and cost. In this research، two novel models on maintenance routing based on flying time (not necessarily duty day) and ramp scheduling are developed. The objectives are cost and delay minimization. The mathematical models are solved using GAMS and sensitivity analyses are presented. The result of sensitivity analysis shows that an increase in the number of aircraft in maintenance model reduces cost and an increase in the number of machines in ramping model reduce delays. So the result shows good performance of the models in reducing costs and delay.

A vehicle routing problem is an important problem that has attracted great attention of researchers in recent years. This paper presents a model for evaluating the efficiency of a periodic vehicle routing problem (PVRP) to get the short routes with maximum sale by providing suitable services to customers before delivering the goods by other competitor distributors. In the goods distribution with short lifetime that customers need a special device for keeping them، the arriving time to customers influence on the sales amount، in which classical VRPs are unable to calculate this kind of assumptions. According to real world applications، the arriving time of the competitors is uncertain because of customer demands، traffic، weather conditions، and the like. A scenario approach is used to handle the uncertainty of the arriving time of rivals. The purpose of this paper is to solve this problem by optimizing the sale of products to customers before delivering the products to other competitor distributors in an uncertain condition by robust optimization. To evaluate the presented model، a number of test problems are solved by two strategies of a differential evolution (DE) algorithm and the results are compared with the results obtained by the CPLEX method in GAMS in small and medium sizes. To evaluate the proposed algorithm for solving large-scale problems، some problems are carried out and the results are compared in term of their accuracy. The computational results represent the capability of the proposed DE strategies in solving large-scale problems in a reasonable time.

The scheduling problems application in today’ s competitive world and usage range of its result in industry is indicating its high importance. One of the important issues in the field of flexible job-shop production scheduling is reverse flows within a single production unit، as is the case in the assembly/disassembly plants. In this paper، we conduct a study of the flexible job-shop scheduling with reverse flows approach which consists of two flows of jobs at each stage in opposite directions. The problem can be used only if you have two flows: The first one going from first stage to last stage، and the second flow going from last stage to first stage. We present a mathematical model of problem with the objective is to minimize the maximal completion time of the jobs (i. e.، the makespan). Because of the complexity solving and prove that this problem ranked on NP-hard problems، we proposed meta-heuristic algorithm genetic (GA) and then design proposed model chromosome structure. Also، The parameters of these algorithm GA and their appropriate operators are set and determined by the use of the Taguchi experimental design. The computational results validate outperforms proposed algorithm GA.

To develop an organization، its units must be continuously evaluated and according to the results of evaluations awarded. Without these policies، the inefficient units are located on the margin of safety and the efficient units are discouraged. In this study، a fair way is presented to distribute a common reward among the units of organization. Using data envelopment analysis، this method determines the share of each unit relative to its performance. To this end، the minimum and maximum possible shares of each DMU are determined، then، a combination of them is used for determining its final share. In this method، the shares are objectively determined.

In a hospital، operating rooms produces a large part of the costs، on the one hand، and a large part of the income، on the other hand. One of the most impressive ways to increase the operating rooms efficiency is using effective ways for planning and scheduling. While this department has a close relation with other departments in the hospital، improving it’ s efficiency will raise patients satisfaction and the whole hospital efficiency and performance. In this paper، an stochastic integer mathematical model has been developed for operating rooms planning and scheduling، based on Fugener et al model، with the aim of minimizing the cost of underutilization and overcapacity in downstream units، including intensive care unit and wards. The model aims to provide a cyclic master surgery scheduling at the tactical level based on hospital strategic decisions. The model allocates blocks to specialties based on block scheduling strategy، but also determines each surgeon’ s surgery schedule. Also، to improve the efficiency and reduce the complexity of the model for the large scale cases، the convolution stochastic distribution parts of the model have been exchanged with the expected values and variances of the capacities needed for different days of the week in a corresponding integer goal programming model. Then، different case studies have been generated by changing some of the parameters to show the objective function sensitivity to the changes. Innovation of this research compare to the Fugener et al study is providing the schedules for the surgeons، instead of the specialties. This saves time، expenses and computational operations. Also، positive half variance of the number of patients in downstream units in each day of the week has been used directly instead of the variance of the number of patients in these units.

Intensity Modulated Radiation Therapy is one of the most commonly used procedures of delivering radiation to cancerous tissues، with the aim of reaching the prescripted dose in the target volume while minimizing damage to nearby healthy organs. Two decisions are of fundamental importance: to select the beam angles and calculate the beam intensities. Beam Angle selection (Beam Angle Optimization) is an important problem and most of the time، beam angles are manually selecteld based on human experience and intuition. In this study، we propose a hybrid approach for automated beam angle selection and computing beam intensities، that aims to find good feasible solutions quickly and then used the solution as a starting point for Local Neighborhood Search to find local optimal solution. Numerical results demonstrated that hybrid approaches in comparison with their corresponding stand-alone methods، quickly and consistently find good local optimal solutions.

In this research، we develop an economic production quantity model in a three layers supply chain with two different structures. Aforementioned chain composed of a supplier، a manufacturer and multiple retailers. In this chain، the supplier transforms raw material to the semi-finished product and sends them to the manufacturer and then the manufacturer transmutes them to the finished product and delivers to the retailers in order to satisfy market demand. The retailers replenish their inventory at the same time and demand of each retailer is different due to essence of various demand customers and according to the decisions of the chain’ s members، two structures of non-integrated and integrated supply chains are surveyed. In this research، we will use the Stackelberg approach to solve the presented models. The ordering cycle of retailers is the decision variable of the model. The main aim of this study is to develop an inventory and production model in three layers supply chains in order to minimize the total cost of chain by utilizing the optimal inventory control policy. At last، numerical examples are presented for each structure of supply chains.